Excessive G–U transversions in novel allele variants in SARS-CoV-2 genomes

Introduction

SARS-CoV-2 is a novel coronavirus that causes an infectious respiratory disease called COVID-19 (Wu et al., 2020). SARS-CoV-2 is closely related to the bat coronavirus RaTG13 with around 96% whole genome nucleotide sequence identity (Zhou et al., 2020). At the complete genome scale it also shares 93.3% identity with the bat-derived coronavirus RmYN02, with 97.2% identity in the 1ab gene (Zhou et al., 2020). Since the discovery of SARS-CoV-2, its evolution became of particular interest to biologists because of the amount of sequencing data that was produced and the importance of this data to provide insights on the determinants of viral pathogenicity (Gussow et al., 2020) and adaptations to human hosts (Van Dorp et al., 2020). SARS-CoV-2 is also quite interesting from a genomics prospective, with an extreme deficiency of genomic CpG dinucleotides of debatable origin (Xia, 2020).

Comparative analysis of genomic data favors the natural origin of SARS-CoV-2, accompanied by natural selection either before or after zoonotic transfer directly from bats or through an intermediate host (Andersen et al., 2020). In either scenario, SARS-CoV-2 would be exposed to both a novel evolutionary landscape that affects the fitness of its genetic variants and novel cellular conditions that could affect its mutation rates directly. For example, it is known, that bats have evolved a number of adaptations including superior resistance to oxidative stress (Chionh et al., 2019) that allow them to harbor multiple viruses without getting the corresponding diseases (Schountz et al., 2017). In light of this, we decided to investigate if the relative mutation frequencies in SARS-CoV-2 changed after its transmission to human hosts.

We compared the relative frequencies of single nucleotide variations (which we will refer to as mutations) in SARS-CoV-2 with the relative frequencies of substitutions that it acquired since the divergence with its last common ancestor with a closely related coronavirus from bats RaTG13. In our terminology, substitutions occurred before zoonotic transmission, while mutations were acquired after. A similar analysis was performed for SARS-CoV and HKU1 coronaviruses.

We found that SARS-CoV-2 has an over 9-fold excess of G–U mutations over substitutions. This effect was much weaker in a similar analysis for SARS-CoV and was not present for HKU1. On the other hand, the substitution profile of SARS-CoV-2 turned out to be quite similar to that of the other coronaviruses, lending further support to existing scenarios of its natural origin (Andersen et al., 2020) and suggesting that the changes in SARS-CoV-2 mutation frequencies have accompanied its transition to human hosts.

Methods

Results and Discussion

We identified 1,251, 1,128 and 2,039 single nucleotide variants in available SARS-CoV-2, SARS-CoV and HKU1 genomes. We assumed that these are mutations that occurred after zoonotic transfer to human hosts. Mutations are deviations of individual genomes from the consensus sequences, but it should be noted that our SARS-CoV-2 consensus had only five nucleotide differences from the reference genome.

In the corresponding genomes we also identified 450, 499 and 3,029 (Method 1, with parsimony-reconstructed ancestral states) and 1,141, 1,237 and 6,514 (Method 2) single nucleotide substitutions. We assume that these substitutions occurred before zoonotic transfer. Substitutions are deviations in the reference genome from the predicted ancestral states (Method 1) or deviations in the reference genome from a closely related animal coronavirus genome (Method 2).

Figure 1 shows the proportion of each mutation/substitution type (substitutions based on Method 1) among all mutations/substitutions. The relative substitution frequencies between SARS-CoV-2 and RaTG13 appear to be similar to those between other human coronaviruses and their relatives, consistent with the natural emergence of the SARS-CoV-2 virus (Andersen et al., 2020).

Tables 1 and 2 give a more detailed view of the number of mutations and substitutions in SARS-CoV-2 (Methods 1 and 2 respectively). P-values are based on a two-tailed Fishers exact test.

Out of 1,251 SARS-CoV-2 novel variants, 193 (15.4%) are G–U transversions, which is over 9.5-fold greater comparing to 7/450 (1.56%, Method 1) or 20/1,141 (1.75%, Method 2) G–U substitutions (P < 0.0001/12, two-tailed Fishers exact test, with Bonferroni correction for 12 multiple comparisons).

This effect was not found for SARS-CoV (41/1,128 or 3.63% mutations vs. 11/499 or 2.2% substitutions, Method 1 vs. 33/1,237 or 2.67% substitutions, Method 2) or HKU1 (89/2,039 or 4.36% mutations vs. 253/3,029 or 8.35% substitutions, Method 1 vs. 625/6,514 or 9.59% substitutions, Method 2).

Most SARS-CoV-2 genomes are currently sequenced using Illumina platforms. To rule out possible sequencing artifacts we performed several subanalysis. First, we searched for mutations in SARS-CoV-2 genomes that were sequenced using Ion Torrent (30 genomes) or Oxford Nanopore (192 genomes). Although the mutation sample size was small, the fraction of G–U mutations was similar to the rest of the data: 13/79 or 16.4% (Ion Torrent) and 30/199 or 15.1% (Oxford Nanopore) G–U mutations.

Rayko & Komissarov (2020) have reported a lower transition/transversion ratio in singleton SARS-CoV-2 genomic variations (that can only be seen in one genome submission). As a separate control, we looked at mutations that were identified in two or more independent genome assemblies. This yielded a similar high proportion of G–U mutations (49/360 or 13.6%). Masking of the first and last 100 nucleotides of the alignments or removing all sequences with at least one “N” letter resulted in 16% (188/1,173) or 14.87% (132/888) G–U mutations correspondingly. De Maio et al. (2020) reported other sequencing issues of SARS-CoV-2 genomes, such as particular highly-mutable sites that might be recurring artifacts. However, the number of such reported sites is too low to affect our results as we counted several single nucleotide variations of the same type in one site as the result of one mutation.

We wanted to see if the effect is robust to other subsamples, such as SARS-CoV-2 genomes sequenced in USA or China. For USA genomes, we obtained 15.1% G–U mutations (159/1,053). For Chinese genomes, the mutation sample size was very low, however the effect was similar (20.5% or 17 out of 83 mutations are G–U).

Among the 193 G–U mutations in SARS-CoV-2, 21 are outside of coding regions, 21 are synonymous and 151 are nonsynonymous. We found no nonsense G–U mutations. Coordinates of all G–U mutations are available in Table S2.

There are two remarkable observations regarding the excess of G–U transversions in SARS-CoV-2. One is that it probably reflects a change in SARS-CoV-2 mutation rates after zoonotic transfer to humans, since the proportion of G–U substitutions measured between the SARS-CoV-2 and the bat coronavirus RaTG13 is unremarkable.

The second remarkable feature is that this excess of mutations is asymmetric: there is no similar effect for C–A mutations. SARS-CoV-2 is a positive (+) RNA strand virus. The copying of positive and negative strands of coronavirus RNA is executed by the same enzymes (Sola et al., 2015). If RNA copying was prone to G–U errors when creating the positive strand, the same mechanism would be expected to introduce G–U errors when copying the negative strand, resulting in additional C–A errors on the positive strand. Note that in SARS-CoV-2 the G (19.6%) and C (18.4%) content are similar, as are A (29.9%) and U (32.1%) content. Recently it was suggested that SARS-CoV-2 mutation rates could be affected by variations in its RNA-dependent RNA polymerase (Pachetti et al., 2020), but it is unclear how this could explain the asymmetric increase of G–U transversions.

The most known example of mutation bias is the excess of C–T mutations in the CpG context in the genomes of many animals (Cooper & Krawczak, 1989), although other important mutation contexts exist (Panchin et al., 2011). Usually in such cases, the excess of complementary mutations (such as G–A in the case of CpG context) is also present and is of the same magnitude.

In theory, strand-specific RNA editing could cause the observed mutation asymmetry. Recently, Nanopore experiments suggested that SARS-CoV-2 has unique RNA-editing sites (Kim et al., 2020). However, this editing was associated with the second position of the AAGAA motif. We checked if this or any motif was present near G–U mutations in the SARS-CoV-2 genome, but found none (Fig. S2).

Di Giorgio et al. (2020) showed the importance of RNA editing in the SARS-CoV-2 genome-wide mutagenesis. They analyzed multiple cDNA reads of viruses from three patients for signs of RNA editing. They found excessive C–U and G–A SNV’s, which could be derived from human APOBEC-mediated C–U deamination (Blanc & Davidson, 2010). They also found excessive A–G and U–C changes that could be derived from deamination of adenosine to inosine mediated by ADAR (Samuel, 2011). These A–G and U–C changes were the most predominant. However, in one of three patients, with the highest read coverage, G–U SNVs were also abundant. In the other two patients, the read coverage was much lower, so it is difficult to conclude if there was a difference in SARS-CoV-2 mutation rates between patients. Interestingly, the data provided by Di Giorgio et al. (2020) also reveals G–U and C–A mutation asymmetry not only in SARS-CoV-2 but also in MERS-CoV.

One notable cause of G–T mutations in DNA is due to reactive oxygen species that generate 8-oxoguanine (8-oxoG) (Ohno et al., 2014), which can be paired not only with cytosines, but also with adenines, resulting in nucleotide mis-incorporation during DNA synthesis (Dai et al., 2018). The same mechanism may lead to errors during RNA synthesis as well (Li, Wu & Deleo, 2006), perhaps even more so, considering that RNA is more prone to oxidative damage than DNA under similar conditions (Li, Wu & Deleo, 2006). It is known that 8-oxoG is involved in transcriptional mutagenesis (Dai et al., 2018) and oxidative stress is associated with respiratory viral infections (Delgado-Roche & Mesta, 2020). In addition, Schneider et al. (1993) reported 8-oxoG formation in isolated RNA of RNA bacteriophages induced by reactive oxygen species.

If higher levels of 8-oxoG generation were associated with peak concentrations of (+) SARS-CoV-2 RNAs in infected cells (at later stages of the infection at the cellular level), 8-oxoG-rich (+) RNA would transfer to new cells, leading to G–A mispairing during subsequent (−) RNA synthesis. This could hypothetically lead to the observed G–U and C–A mutation asymmetry. Bats have evolved increased resistance to oxidative stress (Chionh et al., 2019), which could explain why the excess of G–U substitutions is not observed between SARS-CoV-2 and bat coronavirus RaTG13. It is unclear, however, why SARS-CoV-2 is different from SARS-CoV in this regard. We believe this hypothesis requires further investigation.

Conclusions

We report a 9-fold asymmetrical G–U (+) strand or C–A (−) strand mutation bias in SARS-CoV-2. This feature cannot be traced in the substitution data that reflects the virus’s evolutionary history before its transmission to our species. The observed effect points to a recently acquired change in SARS-CoV-2 mutation pattern or difference in its pathology in humans and bats. Additional studies are warranted to pinpoint the mechanism by which this mutation bias is introduced and how its asymmetry is maintained.

Supplemental Information